Overview

Dataset statistics

Number of variables17
Number of observations360
Missing cells695
Missing cells (%)11.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory47.9 KiB
Average record size in memory136.4 B

Variable types

Numeric8
Categorical7
Boolean2

Alerts

Model Name has a high cardinality: 311 distinct valuesHigh cardinality
df_index is highly overall correlated with Unnamed: 0 and 1 other fieldsHigh correlation
Unnamed: 0 is highly overall correlated with df_index and 1 other fieldsHigh correlation
Current Price is highly overall correlated with Original Price and 1 other fieldsHigh correlation
Original Price is highly overall correlated with Current Price and 2 other fieldsHigh correlation
Discount Percentage is highly overall correlated with Current Price and 1 other fieldsHigh correlation
Brand is highly overall correlated with df_index and 2 other fieldsHigh correlation
Touchscreen is highly overall correlated with Original Price and 2 other fieldsHigh correlation
Bluetooth is highly overall correlated with Original Price and 2 other fieldsHigh correlation
Display Size is highly overall correlated with TouchscreenHigh correlation
Weight is highly overall correlated with BluetoothHigh correlation
Touchscreen is highly imbalanced (70.7%)Imbalance
Bluetooth is highly imbalanced (93.0%)Imbalance
Current Price has 6 (1.7%) missing valuesMissing
Original Price has 56 (15.6%) missing valuesMissing
Discount Percentage has 56 (15.6%) missing valuesMissing
Rating has 4 (1.1%) missing valuesMissing
Number OF Ratings has 45 (12.5%) missing valuesMissing
Model Name has 30 (8.3%) missing valuesMissing
Dial Shape has 100 (27.8%) missing valuesMissing
Strap Color has 100 (27.8%) missing valuesMissing
Strap Material has 56 (15.6%) missing valuesMissing
Touchscreen has 31 (8.6%) missing valuesMissing
Battery Life (Days) has 30 (8.3%) missing valuesMissing
Bluetooth has 5 (1.4%) missing valuesMissing
Display Size has 27 (7.5%) missing valuesMissing
Weight has 149 (41.4%) missing valuesMissing
Model Name is uniformly distributedUniform
df_index has unique valuesUnique
Unnamed: 0 has unique valuesUnique

Reproduction

Analysis started2024-04-14 17:12:07.508592
Analysis finished2024-04-14 17:12:30.587624
Duration23.08 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

df_index
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct360
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean222.70278
Minimum0
Maximum448
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-04-14T22:42:30.843517image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21.85
Q1111.75
median222.5
Q3336.25
95-th percentile423.05
Maximum448
Range448
Interquartile range (IQR)224.5

Descriptive statistics

Standard deviation130.63291
Coefficient of variation (CV)0.5865796
Kurtosis-1.2211219
Mean222.70278
Median Absolute Deviation (MAD)112.5
Skewness0.012368302
Sum80173
Variance17064.956
MonotonicityNot monotonic
2024-04-14T22:42:31.173062image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
407 1
 
0.3%
223 1
 
0.3%
347 1
 
0.3%
146 1
 
0.3%
125 1
 
0.3%
419 1
 
0.3%
357 1
 
0.3%
329 1
 
0.3%
314 1
 
0.3%
265 1
 
0.3%
Other values (350) 350
97.2%
ValueCountFrequency (%)
0 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
448 1
0.3%
447 1
0.3%
445 1
0.3%
444 1
0.3%
443 1
0.3%
441 1
0.3%
440 1
0.3%
438 1
0.3%
437 1
0.3%
436 1
0.3%

Unnamed: 0
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct360
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean222.70278
Minimum0
Maximum448
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-04-14T22:42:31.501194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21.85
Q1111.75
median222.5
Q3336.25
95-th percentile423.05
Maximum448
Range448
Interquartile range (IQR)224.5

Descriptive statistics

Standard deviation130.63291
Coefficient of variation (CV)0.5865796
Kurtosis-1.2211219
Mean222.70278
Median Absolute Deviation (MAD)112.5
Skewness0.012368302
Sum80173
Variance17064.956
MonotonicityNot monotonic
2024-04-14T22:42:31.829654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
407 1
 
0.3%
223 1
 
0.3%
347 1
 
0.3%
146 1
 
0.3%
125 1
 
0.3%
419 1
 
0.3%
357 1
 
0.3%
329 1
 
0.3%
314 1
 
0.3%
265 1
 
0.3%
Other values (350) 350
97.2%
ValueCountFrequency (%)
0 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
448 1
0.3%
447 1
0.3%
445 1
0.3%
444 1
0.3%
443 1
0.3%
441 1
0.3%
440 1
0.3%
438 1
0.3%
437 1
0.3%
436 1
0.3%

Brand
Categorical

Distinct18
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
fire-boltt
45 
noise
34 
boat
30 
zebronics
28 
garmin
27 
Other values (13)
196 

Length

Max length10
Median length7
Mean length6.4527778
Min length4

Characters and Unicode

Total characters2323
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st rowzebronics
2nd rowfire-boltt
3rd rowfire-boltt
4th rowboat
5th rowfire-boltt

Common Values

ValueCountFrequency (%)
fire-boltt 45
12.5%
noise 34
9.4%
boat 30
 
8.3%
zebronics 28
 
7.8%
garmin 27
 
7.5%
pebble 27
 
7.5%
apple 24
 
6.7%
dizo 22
 
6.1%
samsung 21
 
5.8%
fitbit 19
 
5.3%
Other values (8) 83
23.1%

Length

2024-04-14T22:42:32.132370image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fire-boltt 45
12.5%
noise 34
9.4%
boat 30
 
8.3%
zebronics 28
 
7.8%
garmin 27
 
7.5%
pebble 27
 
7.5%
apple 24
 
6.7%
dizo 22
 
6.1%
samsung 21
 
5.8%
fitbit 19
 
5.3%
Other values (8) 83
23.1%

Most occurring characters

ValueCountFrequency (%)
i 253
10.9%
e 229
9.9%
o 215
 
9.3%
b 189
 
8.1%
t 173
 
7.4%
a 164
 
7.1%
s 147
 
6.3%
r 140
 
6.0%
n 130
 
5.6%
l 113
 
4.9%
Other values (11) 570
24.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2278
98.1%
Dash Punctuation 45
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 253
11.1%
e 229
10.1%
o 215
9.4%
b 189
 
8.3%
t 173
 
7.6%
a 164
 
7.2%
s 147
 
6.5%
r 140
 
6.1%
n 130
 
5.7%
l 113
 
5.0%
Other values (10) 525
23.0%
Dash Punctuation
ValueCountFrequency (%)
- 45
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2278
98.1%
Common 45
 
1.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 253
11.1%
e 229
10.1%
o 215
9.4%
b 189
 
8.3%
t 173
 
7.6%
a 164
 
7.2%
s 147
 
6.5%
r 140
 
6.1%
n 130
 
5.7%
l 113
 
5.0%
Other values (10) 525
23.0%
Common
ValueCountFrequency (%)
- 45
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2323
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 253
10.9%
e 229
9.9%
o 215
 
9.3%
b 189
 
8.1%
t 173
 
7.4%
a 164
 
7.1%
s 147
 
6.3%
r 140
 
6.0%
n 130
 
5.6%
l 113
 
4.9%
Other values (11) 570
24.5%

Current Price
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct143
Distinct (%)40.4%
Missing6
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean12514.379
Minimum1199
Maximum98990
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-04-14T22:42:32.444575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1199
5-th percentile1499
Q12126
median3999
Q317367.25
95-th percentile45695.85
Maximum98990
Range97791
Interquartile range (IQR)15241.25

Descriptive statistics

Standard deviation16914.979
Coefficient of variation (CV)1.3516435
Kurtosis6.0094978
Mean12514.379
Median Absolute Deviation (MAD)2252.5
Skewness2.3016886
Sum4430090
Variance2.861165 × 108
MonotonicityNot monotonic
2024-04-14T22:42:32.806710image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1999 25
 
6.9%
1799 20
 
5.6%
2499 15
 
4.2%
3999 15
 
4.2%
3499 12
 
3.3%
1499 12
 
3.3%
2999 11
 
3.1%
8999 7
 
1.9%
2199 6
 
1.7%
3299 6
 
1.7%
Other values (133) 225
62.5%
ValueCountFrequency (%)
1199 5
 
1.4%
1299 6
 
1.7%
1399 3
 
0.8%
1449 1
 
0.3%
1499 12
3.3%
1599 4
 
1.1%
1699 6
 
1.7%
1794 1
 
0.3%
1799 20
5.6%
1897 1
 
0.3%
ValueCountFrequency (%)
98990 1
0.3%
89900 1
0.3%
89490 1
0.3%
82990 1
0.3%
78490 1
0.3%
76990 1
0.3%
73900 1
0.3%
67490 1
0.3%
64990 1
0.3%
55900 1
0.3%

Original Price
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct92
Distinct (%)30.3%
Missing56
Missing (%)15.6%
Infinite0
Infinite (%)0.0%
Mean14415.836
Minimum1669
Maximum96390
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-04-14T22:42:33.161483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1669
5-th percentile3999
Q15999
median7994.5
Q317996
95-th percentile44990
Maximum96390
Range94721
Interquartile range (IQR)11997

Descriptive statistics

Standard deviation15613.457
Coefficient of variation (CV)1.0830768
Kurtosis8.8707182
Mean14415.836
Median Absolute Deviation (MAD)2495.5
Skewness2.734265
Sum4382414
Variance2.4378005 × 108
MonotonicityNot monotonic
2024-04-14T22:42:33.505814image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5999 42
 
11.7%
9999 22
 
6.1%
4999 20
 
5.6%
7999 19
 
5.3%
6999 15
 
4.2%
3999 14
 
3.9%
7990 11
 
3.1%
6990 9
 
2.5%
7499 8
 
2.2%
11999 7
 
1.9%
Other values (82) 137
38.1%
(Missing) 56
15.6%
ValueCountFrequency (%)
1669 1
 
0.3%
1868 1
 
0.3%
1899 1
 
0.3%
2199 1
 
0.3%
2999 1
 
0.3%
3299 2
 
0.6%
3499 1
 
0.3%
3799 2
 
0.6%
3999 14
3.9%
4199 2
 
0.6%
ValueCountFrequency (%)
96390 1
 
0.3%
93990 1
 
0.3%
89900 1
 
0.3%
89460 1
 
0.3%
82990 1
 
0.3%
67990 1
 
0.3%
62490 1
 
0.3%
55990 1
 
0.3%
54999 1
 
0.3%
51990 3
0.8%

Discount Percentage
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct225
Distinct (%)74.0%
Missing56
Missing (%)15.6%
Infinite0
Infinite (%)0.0%
Mean47.955347
Minimum-79.688436
Maximum91.00455
Zeros0
Zeros (%)0.0%
Negative5
Negative (%)1.4%
Memory size2.9 KiB
2024-04-14T22:42:33.834364image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-79.688436
5-th percentile4.3359525
Q133.177427
median53.068408
Q366.67778
95-th percentile78.46322
Maximum91.00455
Range170.69299
Interquartile range (IQR)33.500353

Descriptive statistics

Standard deviation24.696899
Coefficient of variation (CV)0.51499783
Kurtosis1.589673
Mean47.955347
Median Absolute Deviation (MAD)15.779116
Skewness-0.98363234
Sum14578.425
Variance609.93684
MonotonicityNot monotonic
2024-04-14T22:42:34.164860image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66.67777963 11
 
3.1%
74.98122653 5
 
1.4%
64.01280256 5
 
1.4%
50.00833472 4
 
1.1%
28.70410059 4
 
1.1%
55.01375344 4
 
1.1%
70.01166861 4
 
1.1%
50.00625078 3
 
0.8%
82.84692418 3
 
0.8%
60.0060006 3
 
0.8%
Other values (215) 258
71.7%
(Missing) 56
 
15.6%
ValueCountFrequency (%)
-79.68843619 1
0.3%
-20.42017931 1
0.3%
-20.00160013 1
0.3%
-17.71948608 1
0.3%
-17.66090249 1
0.3%
0.003039514 1
0.3%
0.105318589 1
0.3%
0.378619154 1
0.3%
0.735402265 1
0.3%
0.836120401 1
0.3%
ValueCountFrequency (%)
91.00455023 1
 
0.3%
88.34069506 1
 
0.3%
87.00870087 1
 
0.3%
86.84667614 1
 
0.3%
83.76047006 1
 
0.3%
82.84692418 3
0.8%
82.00820082 2
0.6%
81.41630901 1
 
0.3%
79.17326444 2
0.6%
78.8976553 1
 
0.3%

Rating
Real number (ℝ)

Distinct27
Distinct (%)7.6%
Missing4
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean4.0275281
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-04-14T22:42:34.490321image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.5
Q13.9
median4.1
Q34.3
95-th percentile4.7
Maximum5
Range4
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.5568276
Coefficient of variation (CV)0.13825542
Kurtosis3.9850691
Mean4.0275281
Median Absolute Deviation (MAD)0.2
Skewness-1.5890427
Sum1433.8
Variance0.31005697
MonotonicityNot monotonic
2024-04-14T22:42:34.773929image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
4.2 50
13.9%
4.1 47
13.1%
3.9 37
10.3%
4 31
8.6%
4.7 29
8.1%
4.3 28
 
7.8%
3.8 18
 
5.0%
2.5 16
 
4.4%
4.5 15
 
4.2%
3.7 15
 
4.2%
Other values (17) 70
19.4%
ValueCountFrequency (%)
1 1
 
0.3%
2 1
 
0.3%
2.3 1
 
0.3%
2.4 1
 
0.3%
2.5 16
4.4%
2.6 1
 
0.3%
2.7 1
 
0.3%
2.9 1
 
0.3%
3 1
 
0.3%
3.1 5
 
1.4%
ValueCountFrequency (%)
5 7
 
1.9%
4.8 1
 
0.3%
4.7 29
8.1%
4.6 15
 
4.2%
4.5 15
 
4.2%
4.4 14
 
3.9%
4.3 28
7.8%
4.2 50
13.9%
4.1 47
13.1%
4 31
8.6%

Number OF Ratings
Real number (ℝ)

Distinct209
Distinct (%)66.3%
Missing45
Missing (%)12.5%
Infinite0
Infinite (%)0.0%
Mean10671.816
Minimum1
Maximum275607
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-04-14T22:42:35.131656image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q155
median830
Q37576.5
95-th percentile55800
Maximum275607
Range275606
Interquartile range (IQR)7521.5

Descriptive statistics

Standard deviation27575.957
Coefficient of variation (CV)2.5839986
Kurtosis39.817018
Mean10671.816
Median Absolute Deviation (MAD)825
Skewness5.4676303
Sum3361622
Variance7.604334 × 108
MonotonicityNot monotonic
2024-04-14T22:42:35.442888image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 9
 
2.5%
2847 7
 
1.9%
5 7
 
1.9%
10 6
 
1.7%
22 6
 
1.7%
6 6
 
1.7%
1519 5
 
1.4%
567 4
 
1.1%
32704 4
 
1.1%
5502 4
 
1.1%
Other values (199) 257
71.4%
(Missing) 45
 
12.5%
ValueCountFrequency (%)
1 2
 
0.6%
2 3
 
0.8%
3 9
2.5%
4 3
 
0.8%
5 7
1.9%
6 6
1.7%
7 4
1.1%
8 1
 
0.3%
10 6
1.7%
11 2
 
0.6%
ValueCountFrequency (%)
275607 1
 
0.3%
219512 1
 
0.3%
142612 1
 
0.3%
125524 1
 
0.3%
110067 1
 
0.3%
98388 1
 
0.3%
93075 1
 
0.3%
80483 1
 
0.3%
71481 3
0.8%
69803 1
 
0.3%

Model Name
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct311
Distinct (%)94.2%
Missing30
Missing (%)8.3%
Memory size2.9 KiB
ring
 
4
beast pro
 
4
Wave Call
 
3
Watch Flash
 
3
8.90E+12
 
2
Other values (306)
314 

Length

Max length80
Median length65
Mean length36.084848
Min length3

Characters and Unicode

Total characters11908
Distinct characters76
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique298 ?
Unique (%)90.3%

Sample

1st rowLEATHER fit-650
2nd rowbsw020
3rd rowBSW070
4th rowNINJA PRO MAX
5th rowColorFit Loop Advanced BT Calling with 1.85" display,130+Sports Modes

Common Values

ValueCountFrequency (%)
ring 4
 
1.1%
beast pro 4
 
1.1%
Wave Call 3
 
0.8%
Watch Flash 3
 
0.8%
8.90E+12 2
 
0.6%
BSW030 2
 
0.6%
BSW024 2
 
0.6%
BSW042 2
 
0.6%
BSW043 2
 
0.6%
S4 Max 2
 
0.6%
Other values (301) 304
84.4%
(Missing) 30
 
8.3%

Length

2024-04-14T22:42:35.817906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
watch 84
 
4.5%
with 72
 
3.9%
display 59
 
3.2%
58
 
3.1%
calling 50
 
2.7%
gps 34
 
1.8%
2 32
 
1.7%
bluetooth 30
 
1.6%
amoled 26
 
1.4%
pro 24
 
1.3%
Other values (542) 1381
74.6%

Most occurring characters

ValueCountFrequency (%)
1520
 
12.8%
t 651
 
5.5%
a 650
 
5.5%
i 609
 
5.1%
l 548
 
4.6%
e 543
 
4.6%
r 450
 
3.8%
s 409
 
3.4%
o 400
 
3.4%
n 358
 
3.0%
Other values (66) 5770
48.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6909
58.0%
Uppercase Letter 2176
 
18.3%
Space Separator 1520
 
12.8%
Decimal Number 728
 
6.1%
Other Punctuation 279
 
2.3%
Dash Punctuation 148
 
1.2%
Open Punctuation 51
 
0.4%
Close Punctuation 49
 
0.4%
Math Symbol 31
 
0.3%
Connector Punctuation 17
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 651
 
9.4%
a 650
 
9.4%
i 609
 
8.8%
l 548
 
7.9%
e 543
 
7.9%
r 450
 
6.5%
s 409
 
5.9%
o 400
 
5.8%
n 358
 
5.2%
c 335
 
4.8%
Other values (16) 1956
28.3%
Uppercase Letter
ValueCountFrequency (%)
S 260
 
11.9%
B 182
 
8.4%
C 171
 
7.9%
W 158
 
7.3%
A 136
 
6.2%
T 130
 
6.0%
D 128
 
5.9%
G 123
 
5.7%
P 102
 
4.7%
M 94
 
4.3%
Other values (16) 692
31.8%
Decimal Number
ValueCountFrequency (%)
1 124
17.0%
2 119
16.3%
0 115
15.8%
4 89
12.2%
5 64
8.8%
3 49
 
6.7%
6 48
 
6.6%
7 48
 
6.6%
9 37
 
5.1%
8 35
 
4.8%
Other Punctuation
ValueCountFrequency (%)
, 134
48.0%
. 81
29.0%
" 29
 
10.4%
& 19
 
6.8%
' 8
 
2.9%
/ 5
 
1.8%
* 3
 
1.1%
Math Symbol
ValueCountFrequency (%)
+ 19
61.3%
| 12
38.7%
Space Separator
ValueCountFrequency (%)
1520
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 148
100.0%
Open Punctuation
ValueCountFrequency (%)
( 51
100.0%
Close Punctuation
ValueCountFrequency (%)
) 49
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9085
76.3%
Common 2823
 
23.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 651
 
7.2%
a 650
 
7.2%
i 609
 
6.7%
l 548
 
6.0%
e 543
 
6.0%
r 450
 
5.0%
s 409
 
4.5%
o 400
 
4.4%
n 358
 
3.9%
c 335
 
3.7%
Other values (42) 4132
45.5%
Common
ValueCountFrequency (%)
1520
53.8%
- 148
 
5.2%
, 134
 
4.7%
1 124
 
4.4%
2 119
 
4.2%
0 115
 
4.1%
4 89
 
3.2%
. 81
 
2.9%
5 64
 
2.3%
( 51
 
1.8%
Other values (14) 378
 
13.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11908
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1520
 
12.8%
t 651
 
5.5%
a 650
 
5.5%
i 609
 
5.1%
l 548
 
4.6%
e 543
 
4.6%
r 450
 
3.8%
s 409
 
3.4%
o 400
 
3.4%
n 358
 
3.0%
Other values (66) 5770
48.5%

Dial Shape
Categorical

Distinct6
Distinct (%)2.3%
Missing100
Missing (%)27.8%
Memory size2.9 KiB
Circle
101 
Square
81 
Rectangle
71 
Curved
 
5
Oval
 
1

Length

Max length12
Median length6
Mean length6.8346154
Min length4

Characters and Unicode

Total characters1777
Distinct characters21
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.8%

Sample

1st rowOval
2nd rowCircle
3rd rowSquare
4th rowSquare
5th rowRectangle

Common Values

ValueCountFrequency (%)
Circle 101
28.1%
Square 81
22.5%
Rectangle 71
19.7%
Curved 5
 
1.4%
Oval 1
 
0.3%
Contemporary 1
 
0.3%
(Missing) 100
27.8%

Length

2024-04-14T22:42:36.437300image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T22:42:37.190213image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
circle 101
38.8%
square 81
31.2%
rectangle 71
27.3%
curved 5
 
1.9%
oval 1
 
0.4%
contemporary 1
 
0.4%

Most occurring characters

ValueCountFrequency (%)
e 330
18.6%
r 189
10.6%
l 173
9.7%
c 172
9.7%
a 154
8.7%
C 107
 
6.0%
i 101
 
5.7%
u 86
 
4.8%
S 81
 
4.6%
q 81
 
4.6%
Other values (11) 303
17.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1517
85.4%
Uppercase Letter 260
 
14.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 330
21.8%
r 189
12.5%
l 173
11.4%
c 172
11.3%
a 154
10.2%
i 101
 
6.7%
u 86
 
5.7%
q 81
 
5.3%
t 72
 
4.7%
n 72
 
4.7%
Other values (7) 87
 
5.7%
Uppercase Letter
ValueCountFrequency (%)
C 107
41.2%
S 81
31.2%
R 71
27.3%
O 1
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 1777
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 330
18.6%
r 189
10.6%
l 173
9.7%
c 172
9.7%
a 154
8.7%
C 107
 
6.0%
i 101
 
5.7%
u 86
 
4.8%
S 81
 
4.6%
q 81
 
4.6%
Other values (11) 303
17.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1777
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 330
18.6%
r 189
10.6%
l 173
9.7%
c 172
9.7%
a 154
8.7%
C 107
 
6.0%
i 101
 
5.7%
u 86
 
4.8%
S 81
 
4.6%
q 81
 
4.6%
Other values (11) 303
17.1%

Strap Color
Categorical

Distinct28
Distinct (%)10.8%
Missing100
Missing (%)27.8%
Memory size2.9 KiB
Black
102 
Blue
32 
Grey
30 
Silver
12 
Green
11 
Other values (23)
73 

Length

Max length15
Median length5
Mean length5.1115385
Min length3

Characters and Unicode

Total characters1329
Distinct characters34
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)3.5%

Sample

1st rowBlack
2nd rowBrown
3rd rowBlue
4th rowGrey
5th rowGrey

Common Values

ValueCountFrequency (%)
Black 102
28.3%
Blue 32
 
8.9%
Grey 30
 
8.3%
Silver 12
 
3.3%
Green 11
 
3.1%
Pink 10
 
2.8%
Red 10
 
2.8%
Brown 9
 
2.5%
Gold 7
 
1.9%
White 7
 
1.9%
Other values (18) 30
 
8.3%
(Missing) 100
27.8%

Length

2024-04-14T22:42:37.519879image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
black 102
37.4%
blue 32
 
11.7%
grey 30
 
11.0%
silver 12
 
4.4%
green 12
 
4.4%
pink 12
 
4.4%
gold 11
 
4.0%
red 10
 
3.7%
brown 9
 
3.3%
white 8
 
2.9%
Other values (21) 35
 
12.8%

Most occurring characters

ValueCountFrequency (%)
l 171
12.9%
B 146
11.0%
e 140
10.5%
a 119
 
9.0%
k 114
 
8.6%
c 107
 
8.1%
r 83
 
6.2%
G 57
 
4.3%
i 49
 
3.7%
n 45
 
3.4%
Other values (24) 298
22.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1042
78.4%
Uppercase Letter 273
 
20.5%
Space Separator 13
 
1.0%
Other Punctuation 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 171
16.4%
e 140
13.4%
a 119
11.4%
k 114
10.9%
c 107
10.3%
r 83
8.0%
i 49
 
4.7%
n 45
 
4.3%
u 39
 
3.7%
o 38
 
3.6%
Other values (10) 137
13.1%
Uppercase Letter
ValueCountFrequency (%)
B 146
53.5%
G 57
 
20.9%
P 17
 
6.2%
S 17
 
6.2%
R 13
 
4.8%
W 9
 
3.3%
M 6
 
2.2%
O 3
 
1.1%
C 2
 
0.7%
J 1
 
0.4%
Other values (2) 2
 
0.7%
Space Separator
ValueCountFrequency (%)
13
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1315
98.9%
Common 14
 
1.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 171
13.0%
B 146
11.1%
e 140
10.6%
a 119
9.0%
k 114
8.7%
c 107
 
8.1%
r 83
 
6.3%
G 57
 
4.3%
i 49
 
3.7%
n 45
 
3.4%
Other values (22) 284
21.6%
Common
ValueCountFrequency (%)
13
92.9%
, 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1329
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 171
12.9%
B 146
11.0%
e 140
10.5%
a 119
 
9.0%
k 114
 
8.6%
c 107
 
8.1%
r 83
 
6.2%
G 57
 
4.3%
i 49
 
3.7%
n 45
 
3.4%
Other values (24) 298
22.4%

Strap Material
Categorical

Distinct14
Distinct (%)4.6%
Missing56
Missing (%)15.6%
Memory size2.9 KiB
Silicon
197 
Rubber
27 
Other
 
16
Stainless Steel
 
14
Leather
 
12
Other values (9)
38 

Length

Max length27
Median length7
Mean length7.9802632
Min length5

Characters and Unicode

Total characters2426
Distinct characters29
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)1.0%

Sample

1st rowLeather
2nd rowSilicon
3rd rowSilicon
4th rowSilicon
5th rowSilicon

Common Values

ValueCountFrequency (%)
Silicon 197
54.7%
Rubber 27
 
7.5%
Other 16
 
4.4%
Stainless Steel 14
 
3.9%
Leather 12
 
3.3%
Aluminium 11
 
3.1%
Fluoroelastomer 10
 
2.8%
Thermo Plastic Polyurethene 7
 
1.9%
Fabric 3
 
0.8%
Plastic 2
 
0.6%
Other values (4) 5
 
1.4%
(Missing) 56
 
15.6%

Length

2024-04-14T22:42:37.785515image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
silicon 197
59.3%
rubber 27
 
8.1%
other 16
 
4.8%
stainless 14
 
4.2%
steel 14
 
4.2%
leather 12
 
3.6%
aluminium 11
 
3.3%
fluoroelastomer 10
 
3.0%
plastic 9
 
2.7%
thermo 7
 
2.1%
Other values (6) 15
 
4.5%

Most occurring characters

ValueCountFrequency (%)
i 443
18.3%
l 276
11.4%
o 245
10.1%
n 232
9.6%
S 225
9.3%
c 210
8.7%
e 161
 
6.6%
r 95
 
3.9%
t 85
 
3.5%
u 66
 
2.7%
Other values (19) 388
16.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2063
85.0%
Uppercase Letter 333
 
13.7%
Space Separator 28
 
1.2%
Dash Punctuation 1
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 443
21.5%
l 276
13.4%
o 245
11.9%
n 232
11.2%
c 210
10.2%
e 161
 
7.8%
r 95
 
4.6%
t 85
 
4.1%
u 66
 
3.2%
b 60
 
2.9%
Other values (5) 190
9.2%
Uppercase Letter
ValueCountFrequency (%)
S 225
67.6%
R 27
 
8.1%
P 17
 
5.1%
O 16
 
4.8%
F 14
 
4.2%
L 12
 
3.6%
A 11
 
3.3%
T 7
 
2.1%
M 2
 
0.6%
C 1
 
0.3%
Space Separator
ValueCountFrequency (%)
28
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Other Punctuation
ValueCountFrequency (%)
; 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2396
98.8%
Common 30
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 443
18.5%
l 276
11.5%
o 245
10.2%
n 232
9.7%
S 225
9.4%
c 210
8.8%
e 161
 
6.7%
r 95
 
4.0%
t 85
 
3.5%
u 66
 
2.8%
Other values (16) 358
14.9%
Common
ValueCountFrequency (%)
28
93.3%
- 1
 
3.3%
; 1
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2426
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 443
18.3%
l 276
11.4%
o 245
10.1%
n 232
9.6%
S 225
9.3%
c 210
8.7%
e 161
 
6.6%
r 95
 
3.9%
t 85
 
3.5%
u 66
 
2.7%
Other values (19) 388
16.0%

Touchscreen
Boolean

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.6%
Missing31
Missing (%)8.6%
Memory size852.0 B
True
312 
False
 
17
(Missing)
 
31
ValueCountFrequency (%)
True 312
86.7%
False 17
 
4.7%
(Missing) 31
 
8.6%
2024-04-14T22:42:38.051714image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Battery Life (Days)
Real number (ℝ)

Distinct7
Distinct (%)2.1%
Missing30
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean14.18197
Minimum0.75
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-04-14T22:42:38.312906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.75
5-th percentile3.5
Q18
median17.5
Q322
95-th percentile22
Maximum22
Range21.25
Interquartile range (IQR)14

Descriptive statistics

Standard deviation7.6618782
Coefficient of variation (CV)0.54025487
Kurtosis-1.6928848
Mean14.18197
Median Absolute Deviation (MAD)4.5
Skewness-0.19029764
Sum4680.05
Variance58.704378
MonotonicityNot monotonic
2024-04-14T22:42:38.531914image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
22 141
39.2%
8 89
24.7%
3.5 53
 
14.7%
17.5 31
 
8.6%
12.5 10
 
2.8%
3.6 3
 
0.8%
0.75 3
 
0.8%
(Missing) 30
 
8.3%
ValueCountFrequency (%)
0.75 3
 
0.8%
3.5 53
 
14.7%
3.6 3
 
0.8%
8 89
24.7%
12.5 10
 
2.8%
17.5 31
 
8.6%
22 141
39.2%
ValueCountFrequency (%)
22 141
39.2%
17.5 31
 
8.6%
12.5 10
 
2.8%
8 89
24.7%
3.6 3
 
0.8%
3.5 53
 
14.7%
0.75 3
 
0.8%

Bluetooth
Boolean

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.6%
Missing5
Missing (%)1.4%
Memory size852.0 B
True
352 
False
 
3
(Missing)
 
5
ValueCountFrequency (%)
True 352
97.8%
False 3
 
0.8%
(Missing) 5
 
1.4%
2024-04-14T22:42:38.807386image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Display Size
Categorical

HIGH CORRELATION  MISSING 

Distinct34
Distinct (%)10.2%
Missing27
Missing (%)7.5%
Memory size2.9 KiB
1.7 inches
56 
1.3 inches
50 
1.8 inches
48 
1.4 inches
35 
0.1 inches
28 
Other values (29)
116 

Length

Max length11
Median length10
Mean length10.015015
Min length10

Characters and Unicode

Total characters3335
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)4.5%

Sample

1st row2.7 inches
2nd row1.4 inches
3rd row1.9 inches
4th row1.6 inches
5th row1.9 inches

Common Values

ValueCountFrequency (%)
1.7 inches 56
15.6%
1.3 inches 50
13.9%
1.8 inches 48
13.3%
1.4 inches 35
9.7%
0.1 inches 28
7.8%
1.6 inches 25
6.9%
1.9 inches 21
 
5.8%
1.2 inches 17
 
4.7%
1.5 inches 6
 
1.7%
1.1 inches 6
 
1.7%
Other values (24) 41
11.4%
(Missing) 27
7.5%

Length

2024-04-14T22:42:39.026455image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
inches 333
50.0%
1.7 56
 
8.4%
1.3 50
 
7.5%
1.8 48
 
7.2%
1.4 35
 
5.3%
0.1 28
 
4.2%
1.6 25
 
3.8%
1.9 21
 
3.2%
1.2 17
 
2.6%
1.5 6
 
0.9%
Other values (25) 47
 
7.1%

Most occurring characters

ValueCountFrequency (%)
s 333
10.0%
e 333
10.0%
. 333
10.0%
333
10.0%
i 333
10.0%
n 333
10.0%
c 333
10.0%
h 333
10.0%
1 303
9.1%
3 62
 
1.9%
Other values (8) 306
9.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1998
59.9%
Decimal Number 671
 
20.1%
Other Punctuation 333
 
10.0%
Space Separator 333
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 303
45.2%
3 62
 
9.2%
7 59
 
8.8%
0 50
 
7.5%
8 49
 
7.3%
4 47
 
7.0%
2 32
 
4.8%
9 29
 
4.3%
6 27
 
4.0%
5 13
 
1.9%
Lowercase Letter
ValueCountFrequency (%)
s 333
16.7%
e 333
16.7%
i 333
16.7%
n 333
16.7%
c 333
16.7%
h 333
16.7%
Other Punctuation
ValueCountFrequency (%)
. 333
100.0%
Space Separator
ValueCountFrequency (%)
333
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1998
59.9%
Common 1337
40.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 333
24.9%
333
24.9%
1 303
22.7%
3 62
 
4.6%
7 59
 
4.4%
0 50
 
3.7%
8 49
 
3.7%
4 47
 
3.5%
2 32
 
2.4%
9 29
 
2.2%
Other values (2) 40
 
3.0%
Latin
ValueCountFrequency (%)
s 333
16.7%
e 333
16.7%
i 333
16.7%
n 333
16.7%
c 333
16.7%
h 333
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3335
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 333
10.0%
e 333
10.0%
. 333
10.0%
333
10.0%
i 333
10.0%
n 333
10.0%
c 333
10.0%
h 333
10.0%
1 303
9.1%
3 62
 
1.9%
Other values (8) 306
9.2%

Weight
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)2.4%
Missing149
Missing (%)41.4%
Memory size2.9 KiB
20 - 35 g
63 
75g +
58 
35 - 50 g
45 
<= 20 g
30 
50 - 75 g
15 

Length

Max length9
Median length9
Mean length7.6161137
Min length5

Characters and Unicode

Total characters1607
Distinct characters11
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row75g +
2nd row75g +
3rd row75g +
4th row<= 20 g
5th row20 - 35 g

Common Values

ValueCountFrequency (%)
20 - 35 g 63
17.5%
75g + 58
 
16.1%
35 - 50 g 45
 
12.5%
<= 20 g 30
 
8.3%
50 - 75 g 15
 
4.2%
(Missing) 149
41.4%

Length

2024-04-14T22:42:39.324658image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T22:42:39.668391image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
211
30.2%
g 153
21.9%
35 108
15.5%
20 93
13.3%
50 60
 
8.6%
75g 58
 
8.3%
75 15
 
2.1%

Most occurring characters

ValueCountFrequency (%)
487
30.3%
5 241
15.0%
g 211
13.1%
0 153
 
9.5%
- 123
 
7.7%
3 108
 
6.7%
2 93
 
5.8%
7 73
 
4.5%
+ 58
 
3.6%
< 30
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 668
41.6%
Space Separator 487
30.3%
Lowercase Letter 211
 
13.1%
Dash Punctuation 123
 
7.7%
Math Symbol 118
 
7.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 241
36.1%
0 153
22.9%
3 108
16.2%
2 93
 
13.9%
7 73
 
10.9%
Math Symbol
ValueCountFrequency (%)
+ 58
49.2%
< 30
25.4%
= 30
25.4%
Space Separator
ValueCountFrequency (%)
487
100.0%
Lowercase Letter
ValueCountFrequency (%)
g 211
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 123
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1396
86.9%
Latin 211
 
13.1%

Most frequent character per script

Common
ValueCountFrequency (%)
487
34.9%
5 241
17.3%
0 153
 
11.0%
- 123
 
8.8%
3 108
 
7.7%
2 93
 
6.7%
7 73
 
5.2%
+ 58
 
4.2%
< 30
 
2.1%
= 30
 
2.1%
Latin
ValueCountFrequency (%)
g 211
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1607
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
487
30.3%
5 241
15.0%
g 211
13.1%
0 153
 
9.5%
- 123
 
7.7%
3 108
 
6.7%
2 93
 
5.8%
7 73
 
4.5%
+ 58
 
3.6%
< 30
 
1.9%

Interactions

2024-04-14T22:42:26.321416image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:11.176426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:13.277598image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:15.321937image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:17.426917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:19.588360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:21.706922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:23.955921image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:26.556563image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:11.463553image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:13.510326image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:15.572691image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:17.677327image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:19.839024image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:21.987128image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:24.211914image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:26.802168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:11.698714image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:13.745161image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:15.838651image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:17.982215image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:20.097705image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:22.261447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:24.691752image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:27.068268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:11.949291image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:14.011682image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:16.098624image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:18.227510image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:20.348573image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:22.546386image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:24.957949image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:27.328123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:12.212379image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:14.258884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:16.336187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:18.493930image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:20.624582image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:22.812086image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:25.225788image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:27.579053image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:12.486952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:14.525372image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:16.611690image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:18.768360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:20.890664image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:23.102913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:25.508000image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:27.880516image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:12.777187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:14.823198image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:16.909441image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:19.067266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:21.189214image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:23.409369image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:25.805680image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:28.175919image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:13.031581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:15.073277image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:17.184391image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:19.337684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:21.470625image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:23.689570image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-04-14T22:42:26.071829image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2024-04-14T22:42:39.981674image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
df_indexUnnamed: 0Current PriceOriginal PriceDiscount PercentageRatingNumber OF RatingsBattery Life (Days)BrandDial ShapeStrap ColorStrap MaterialTouchscreenBluetoothDisplay SizeWeight
df_index1.0001.0000.090-0.064-0.230-0.185-0.3410.3390.6790.3490.2610.3420.3730.2350.2930.405
Unnamed: 01.0001.0000.090-0.064-0.230-0.185-0.3410.3390.6790.3490.2610.3420.3730.2350.2930.405
Current Price0.0900.0901.0000.802-0.7930.441-0.363-0.0350.3730.2060.1830.2850.2960.0000.0980.251
Original Price-0.064-0.0640.8021.000-0.3540.359-0.182-0.2510.3830.2300.0000.1620.5281.0000.0940.182
Discount Percentage-0.230-0.230-0.793-0.3541.000-0.2410.400-0.0200.4880.2570.1850.2030.2131.0000.1960.248
Rating-0.185-0.1850.4410.359-0.2411.0000.210-0.0410.3350.1820.0000.0950.1790.2250.3890.264
Number OF Ratings-0.341-0.341-0.363-0.1820.4000.2101.0000.0260.0000.0000.0000.1930.0000.0000.0000.193
Battery Life (Days)0.3390.339-0.035-0.251-0.020-0.0410.0261.0000.4110.1890.1940.3010.1680.1730.1710.314
Brand0.6790.6790.3730.3830.4880.3350.0000.4111.0000.4260.1270.3640.5990.3340.2840.459
Dial Shape0.3490.3490.2060.2300.2570.1820.0000.1890.4261.0000.0000.2460.3020.1090.4510.208
Strap Color0.2610.2610.1830.0000.1850.0000.0000.1940.1270.0001.0000.2480.0000.4860.1230.349
Strap Material0.3420.3420.2850.1620.2030.0950.1930.3010.3640.2460.2481.0000.2350.0000.1900.335
Touchscreen0.3730.3730.2960.5280.2130.1790.0000.1680.5990.3020.0000.2351.0000.0000.6020.000
Bluetooth0.2350.2350.0001.0001.0000.2250.0000.1730.3340.1090.4860.0000.0001.0000.3511.000
Display Size0.2930.2930.0980.0940.1960.3890.0000.1710.2840.4510.1230.1900.6020.3511.0000.388
Weight0.4050.4050.2510.1820.2480.2640.1930.3140.4590.2080.3490.3350.0001.0000.3881.000

Missing values

2024-04-14T22:42:28.624339image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-14T22:42:29.345408image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-04-14T22:42:30.033476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

df_indexUnnamed: 0BrandCurrent PriceOriginal PriceDiscount PercentageRatingNumber OF RatingsModel NameDial ShapeStrap ColorStrap MaterialTouchscreenBattery Life (Days)BluetoothDisplay SizeWeight
0407407zebronics2949.04199.029.7689933.110.0LEATHER fit-650OvalBlackLeatherYesNaNYes2.7 inchesNaN
1444444fire-boltt6999.015999.056.2535162.6NaNbsw020CircleBrownSiliconYes8.0Yes1.4 inchesNaN
2117117fire-boltt2499.011999.079.1732644.11990.0BSW070NaNNaNNaNYes8.0Yes1.9 inches75g +
33030boat1999.07990.074.9812273.6827.0NaNNaNNaNNaNNaN8.0YesNaN75g +
4415415fire-boltt2299.05999.061.6769462.524.0NINJA PRO MAXSquareBlueSiliconYes22.0Yes1.6 inchesNaN
5157157noise2499.06999.064.2948994.33944.0ColorFit Loop Advanced BT Calling with 1.85" display,130+Sports ModesSquareGreySiliconYes22.0Yes1.9 inchesNaN
6325325dizo3499.05999.041.6736124.21785.0Watch D Talk 1.8 display with calling&7 day battery (by realme Techlife)RectangleGreySiliconYes22.0Yes1.8 inches75g +
7447447fire-boltt5999.08999.033.3370372.5NaNbsw003CircleGoldSiliconYes22.0Yes1.3 inchesNaN
8268268garmin20490.0NaNNaN5.03.0Vivomove SportsCircleBrownSiliconNo3.5Yes18.5 inchesNaN
9297297fossil17995.0NaNNaN3.946.0Sport SmartwatchCircleMulticolorSiliconYes17.5Yes1.2 inchesNaN
df_indexUnnamed: 0BrandCurrent PriceOriginal PriceDiscount PercentageRatingNumber OF RatingsModel NameDial ShapeStrap ColorStrap MaterialTouchscreenBattery Life (Days)BluetoothDisplay SizeWeight
3501414noise1999.05999.066.6777804.19081.0wrb-sw-colorfitultrabuzz-std-ogrn_ogrnNaNNaNOtherYesNaNYes1.8 inches35 - 50 g
351156156noise2999.05999.050.0083354.313108.0ColorFit Pro4 BT Calling 1.72" TruView Display, Fully-functional digital crownRectangleBlackSiliconYes22.0Yes1.7 inchesNaN
3524040honor2199.01868.0-17.7194864.1181.0ARG-B39-crNaNNaNNaNYes3.5Yes1.5 inches<= 20 g
353427427fire-boltt3799.09999.062.0062013.7NaNbeast proSquareBlackRubberYes3.5Yes0.1 inchesNaN
354364364pebble1999.04999.060.0120024.04240.0Frost 1.87'' BT Calling with 2.5D Curved HD Display, AI Voice AssisstRectangleBlueSiliconYes3.5Yes20.0 inches20 - 35 g
355216216fitbit11699.014999.022.0014674.33999.0Versa 2SquareBlackSiliconYes3.5Yes1.3 inches20 - 35 g
356279279garmin39490.044990.012.2249394.7109.0Instinct 2, Rugged Outdoor Watch with GPS, Built for All Elements, Multi-GNSSCircleGreySiliconNo17.5Yes0.9 inchesNaN
357390390zebronics2199.04999.056.0112023.9272.0Zeb-Fit MeSquareGreenThermo Plastic PolyuretheneYes22.0Yes3.3 inches20 - 35 g
358337337gizmore1199.04499.073.3496334.7NaNGizFit CLOUD 1.85 IPS Large Display | AI Voice Assistant | Bluetooth CallingSquareBlueSiliconYes22.0Yes1.8 inches20 - 35 g
359236236fitbit20499.0NaNNaN4.7NaNFitbit Versa 4 Fitness Watch (Waterfall Blue / Platinum Aluminium) MembershipCurvedBlueRubberYes22.0Yes0.2 inchesNaN